Financial Benefits from Seven Years of Water Loss Control Utilizing the Sahara System at Thames Water in the United Kingdom
Bibliographic record
Abstract
Thames Water Utilities Limited is the largest water and wastewater services company in the United Kingdom. It serves 13 million customers in London and across the Thames Valley, from Kent and Essex in the east to the edges of Gloucestershire in the west. The utility business treats and supplies an average of approximately 2,700 million liters (713 million US gallons) of water per day. In London as a whole, over a third of mains are more than 150 years old. Over half are more than 100 years old. Thames Water initiated trunk main leakage reduction programs concentrating on unaccounted for water losses in its transmission mains before the distribution network. A range of new and traditional leak detection methods was compared. Parameters included cost of operation, sensitivity of detection and accuracy of location. Thames concluded that the Sahara Leak Location system was the most accurate and cost effective way of detecting and locating trunk main leaks. Consequently, Sahara was used exclusively for subsequent phases. To date, over 960 surveys have been completed and over 960 leaks have been located with Thames Water reporting that, recently, the average leak repaired is approximately 0.15Ml/day (25 US gallons/day) arising mainly from deteriorated lead run joints on cast iron mains and corrosion through the wall of steel mains. In excavating at the identified locations, Thames Water quoted a near 100% accuracy record. This paper discusses the financial benefits to Thames Water from the annual volume of leaks identified within their water transmission system in the eight years from 1998 to 2005, including the cumulative leakage found as well as the approximate leak volume per distance inspected each year.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".